mlsanityData to 7 Oct 2026

FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-aware Model Update

arXiv 2312.05770 ↗First posted 10 Dec 2023cs.DCcited by 32

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Abstract (arXiv)

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 17; 2026: 5.

32 papers.

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  1. 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
  2. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  3. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  4. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  5. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  6. 2025Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustnesscited by 8
  7. 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approachcited by 7
  8. 2024Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Datacited by 6
  9. 2024Fisher Information-based Efficient Curriculum Federated Learning with Large Language Modelscited by 6
  10. 2025SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronizationcited by 4
  11. 2024GAS: Generative Activation-Aided Asynchronous Split Federated Learningcited by 3
  12. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
20 more
  1. 2025SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignmentcited by 3
  2. 2025Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environmentcited by 3
  3. 2025Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possiblecited by 2
  4. 2025Multimodal Online Federated Learning with Modality Missing in Internet of Thingscited by 2
  5. 2025AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learningcited by 2
  6. 2025Efficient Federated Learning with Timely Update Disseminationcited by 2
  7. 2025Efficient Federated Learning with Heterogeneous Data and Adaptive Dropoutcited by 2
  8. 2024Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregationcited by 1
  9. 2025Orthogonal Calibration for Asynchronous Federated Learningcited by 1
  10. 2025Helmsman: Autonomous Synthesis of Federated Learning Systems via Collaborative LLM Agentscited by 1
  11. 2025SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Trainingcited by 0
  12. 2025Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Modelscited by 0
  13. 2025Balancing Client Participation in Federated Learning Using AoIcited by 0
  14. 2025Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learningcited by 0
  15. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0
  16. 2026FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learningcited by 0
  17. 2026SecureAFL: Secure Asynchronous Federated Learningcited by 0
  18. 2026FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Trainingcited by 0
  19. 2026On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approachcited by 0
  20. 2026Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregationcited by 0